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书目名称Grammatical Inference: Algorithms and Applications影响因子(影响力)




书目名称Grammatical Inference: Algorithms and Applications影响因子(影响力)学科排名




书目名称Grammatical Inference: Algorithms and Applications网络公开度




书目名称Grammatical Inference: Algorithms and Applications网络公开度学科排名




书目名称Grammatical Inference: Algorithms and Applications被引频次




书目名称Grammatical Inference: Algorithms and Applications被引频次学科排名




书目名称Grammatical Inference: Algorithms and Applications年度引用




书目名称Grammatical Inference: Algorithms and Applications年度引用学科排名




书目名称Grammatical Inference: Algorithms and Applications读者反馈




书目名称Grammatical Inference: Algorithms and Applications读者反馈学科排名

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Ten Open Problems in Grammatical Inferenceical questions. They cover the areas of polynomial learning models, learning from ordered alphabets, learning deterministic .s, learning negotiation processes, learning from context-free background knowledge.

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Stochastic Analysis of Lexical and Semantic Enhanced Structural Language Modelnt semantic analysis (PLSA) for the purpose of statistical language modeling. The SLM is essentially a generalization of shift-reduce probabilistic push-down automata thus more complex and powerful than probabilistic context free grammars (PCFGs). The added context-sensitiveness due to trigrams and

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Omnipotent

Learning Analysis by Reduction from Positive Datapaper we propose a method for learning restarting automata which are strictly locally testable (.-.-automata). The method is based on the concept of identification in the limit from positive examples only. Also we characterize the class of languages accepted by .-.-automata with respect to the Choms

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